In one of a series of essays on the impact of AI on the insights profession, Martin Rückert describes how you can make the most of artificial intelligence. Train the machine to efficiently support the marketer with human-level precision for concept understanding, so humans can focus on complex tasks like formulating new product ideas.
The human mind is adept at developing complex and unique data schemas based on a new input, but computers are limited in their ability to do so. For marketing, this means that although machines may be able to produce and consolidate the data you need, the human touch is still essential.
A human would be able to interpret the following example finding: 45% of German men like the scent of cedar in their shampoo and turn it into an action: test a new product line of cedar-scented hair care in Germany.
Because computers always need a framework, as well as specific input and output data, they can only perform one of the above tasks (never both). It takes a human brain to interpret “German men like the scent of cedar” and use that knowledge as an impetus for a new campaign.
Humans are able to handle a number of varied and complex problems at the same time using what is called a “working memory”. Our working memories help us reason, guiding our decision-making and behavior. They allow us to store a certain amount of information temporarily and then manipulate that information according to certain rules.
Current machine learning models simply do not resemble such a working memory. You can try to use them to solve complex tasks, but it would be the equivalent to a human trying to think with the part of the brain that is used to see and detect cats. In absence of a working memory, that part of the brain’s capacity to store information and the ability to manipulate that information is too limited to achieve reliable and accurate results that would bring you from “German men like the scent of cedar” to “let’s try a new cedar-scented product line”.
A solution to this problem is the combination between the concept of a working memory (something that has existed in IT for a long time) and the concepts of machine learning. At Market Logic, our strategy is to help humans do the complex task solving and have the machine do the boring work, for example, “see” concepts such as consumers, needs, brands and so on as the baseline for further learning (brands, categories, barriers, drivers, and trends).
As a recent Harvard Business Review article notes, Market Logic and Unilever are effectively using this approach to augment human intelligence in insights engines to deliver real value for marketers. In essence, we enable the machine to employ its strengths (detecting patterns and presenting them for interpretation) to provide the necessary context and information that will enable marketers to effectively drive important business decisions.